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Time Series Analysis Using Wavelets And Gjr-Garch Models

Monica Borda, Mircea Gherman, Romulus Terebes
2012 Zenodo  
CONCLUSIONS AND FUTURE WORK We showed how wavelet analysis and GJR-GARCH prediction can be used as signal processing application for financial time series with only a few parameters to be estimated.  ...  A forecasting method based on the wavelet analysis and GJR-GARCH model is, from the best of our knowledge, not available in the literature.  ... 
doi:10.5281/zenodo.52205 fatcat:anqm2liaabhzll7fvdooec7a2i

Volatility Forecasting: The Support Vector Regression Can Beat the Random Walk

The random walk, GARCH(1,1) and GJR(1,1) on the skewed Student's t-distribution serve as comparison models by using Mean Squared Error (MSE) and the Diebold-Mariano test.  ...  Financial time series prediction is important, and it is a challenger task in empirical finance due to its chaotic, nonlinear and complex nature.  ...  Wavelets functions can approximate a signal and model the frequency and temporal domain of time series by translations and dilations of a mother wavelet Ψ(𝑥) ∈ 𝐿 2 (ℝ 𝑝 ): Ψ 𝑘,𝑎 (𝑥) = 1 √𝑎 Ψ ( 𝑥  ... 
doi:10.24818/18423264/ fatcat:tiaq7hpbeja53pgkfcw57drade

Wavelet Based Detection of Outliers in Volatility Time Series Models

Khudhayr A. Rashedi, Mohd Tahir Ismail, Abdeslam Serroukh, S. Al wadi
2022 Computers Materials & Continua  
The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models  ...  We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.  ...  Acknowledgement: We would like to thank the anonymous referees for their useful comments and efforts towards improving the quality of this manuscript.  ... 
doi:10.32604/cmc.2022.026476 fatcat:27qasmd7hveubmuax465k3w3bi

Existence and extent of impact of individual stock derivatives on spot market volatility in India

Abhilash S. Nair
2011 Applied Financial Economics  
GARCH (GJR GARCH)) models for each stock.  ...  Conditional Heteroscedastic (GARCH) family of models.  ...  The usual disclaimer applies to all remaining errors and omissions  ... 
doi:10.1080/09603107.2010.534061 fatcat:q2ye6cjkxrfy3kxvfjlozsly3q

A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

Hang Yang, Yajie Zou, Zhongyu Wang, Bing Wu
2018 Canadian journal of civil engineering (Print)  
by Markov Chain, and the volatility part estimated by the modified 16 generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model.  ...  This paper proposes a hybrid 8 model which embraces Wavelet Neural Network, Markov Chain and the volatility model (WNN-9 MAR-VOA) for short-term travel time prediction in a freeway system.  ...  Wavelet Neural Network is used to present the periodic pattern, 10 Markov Chain is utilized to correct the residual part of initial prediction, and GJR- GARCH 11 model is employed to predict the volatility  ... 
doi:10.1139/cjce-2017-0231 fatcat:d4vyqtmex5d4nchu7erextkedm

Evidence of Stock Market Contagion during the COVID-19 Pandemic: A Wavelet-Copula-GARCH Approach

Huthaifa Alqaralleh, Alessandra Canepa
2021 Journal of Risk and Financial Management  
In this study, we propose a wavelet-copula-GARCH procedure to investigate the occurrence of cross-market linkages during the COVID-19 pandemic.  ...  To explore cross-market linkages, we distinguish between regular interdependence and pure contagion, and associate changes in the correlation between stock market returns at higher frequencies with contagion  ...  Acknowledgments: We are grateful to three anonymous referees for their useful insights and suggestions.  ... 
doi:10.3390/jrfm14070329 fatcat:pwzcobrerjbgrcms6sulsdec2u

Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets

Phichhang Ou, Hengshan Wang
2010 International Journal of Economics and Finance  
More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially  ...  In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities  ...  Researches on time varying volatility using the time series models have been active ever since Engle introduced the ARCH (autoregressive conditional heteroscedasticity) model in 1982.  ... 
doi:10.5539/ijef.v2n1p51 fatcat:wr4is64xgvdcjcgcm6hezjik4u

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

Maryam Faal, Farshad Almasganj, G R Sinha
2021 Journal of Healthcare Engineering  
Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying.  ...  So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential  ...  GARCH modeling is a statistical method for time series modeling whose variances are a stochastic process widely used in modeling financial time series. e main idea of this model is that the conditional  ... 
doi:10.1155/2021/4894501 pmid:34306589 pmcid:PMC8282402 fatcat:nx2ikd4g5vcutaib4gxtfm5bcy

Outlier Detection Based on Discrete Wavelet Transform with Application to Saudi Stock Market Closed Price Series

Khudhayr A. RASHEDI, Mohd T. ISMAIL, S. Al WADI, Abdeslam SERROUKH
2020 Journal of Asian Finance, Economics and Business  
The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models.  ...  This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important  ...  (Grané & Veiga, 2010) proposed a wavelet based method for outlier detection and correction that can be applied to the residuals of different volatility models such as: the GARCH, the GJR-GARCH and the  ... 
doi:10.13106/jafeb.2020.vol7.no12.001 fatcat:ij4b6jocvbdx5m2mqqovc26qou

Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model

Jamal Bouoiyour, Refk Selmi
2014 Journal of International Trade and Economic Development  
To assess the link between exchange rate uncertainty and exports performance in Egypt, this article relies on an optimal GARCH model chosen by information criteria among decomposed series on a scale-by-scale  ...  basis (wavelet decomposition).  ...  Considering several frequency bands, time series can be extracted for further analysis. Firstly, with wavelets analysis, we can differentiate between time periods for decision making.  ... 
doi:10.1080/09638199.2014.889740 fatcat:qpa3namt3nbtbnxw5b23jyqhy4

COVID-19, Government Response, and Market Volatility: Evidence from the Asia-Pacific Developed and Developing Markets

Izani Ibrahim, Kamilah Kamaludin, Sheela Sundarasen
2020 Economies  
Using the continuous wavelet transformation (CWT) analysis and plots and GJR-GARCH analysis, we examined the effects of the COVID-19 public health crisis and the corresponding government measures on the  ...  The GJR-GARCH results further ascertain that market volatilities are affected by domestic events, notably, the COVID-19 government intervention measures.  ...  GJR-GARCH (1,1) This section provides the results of our analysis using a standard time-series modeling approach examining the relations between the daily COVID-19 cases and the government response and  ... 
doi:10.3390/economies8040105 fatcat:mb6ss3zr6jghhieynv34cyomf4

An Algorithm Using GARCH Process, Monte-Carlo Simulation and Wavelets Analysis for Stock Prediction

Eleftherios Giovanis
2008 Social Science Research Network  
We obtain the stock returns and we would like to predict, not the actual price , but the sign of stock returns.  ...  This paper examines and presents a simple algorithm for prediction stock written in MATLAB code. We apply it to thirty stocks of the Athens exchange stock market .  ...  I INTRODUCTION We use GARCH model because financial time series are characterized by leptokurtosis , clustering volatility and leverage effects.  ... 
doi:10.2139/ssrn.1271248 fatcat:2bahzdz7crdtvdjombx5ol25fe

Advances in Statistical Forecasting Methods: An Overview

Rumana Majid
2018 Economic Affairs  
In this review we summarized the various statistical methods used for forecasting purposes starting from the basic function to complex function in order to evaluate various data sets viz-a-viz time series  ...  From simple additive to multiplicative effects and then automated functions were used to evaluate the complexity in data for forecasting purpose.  ...  Oliveira et al. (2012) used ARIMA models in their study to determine the usefulness of time series models in the analysis of agricultural products prices.  ... 
doi:10.30954/0424-2513.4.2018.5 fatcat:g4ypncvexzecdcwvidxwoqizuq

Modelling Financial Market Volatility Using Asymmetric-Skewed-ARFIMAX and -HARX Models

Wen Cheong Chin, Min Cherng Lee, Grace Lee Ching Yap
2016 Engineering Economics  
In order to capture volatility clustering and the asymmetric property of various realized volatilities, the HAR and ARFIMA models are extended with asymmetric GARCH threshold specification.  ...  Extended heterogeneous autoregressive (HAR) and fractionally integrated autoregressive moving average (ARFIMA) models are introduced to model the S&P500 index using various realized volatility measures  ...  The extended models are named as asymmetric skewed HARX (RV)-GJR-GARCH and ARFIMAX (RV)-GJR-GARCH which will be demonstrated using the S&P500 index.  ... 
doi:10.5755/ fatcat:t6cwvr2uvng4nbsqucveet37aq

Tracing Causality and Co-movement between Pakistani and the Leading Foreign Stock Markets: A Graph Theoretic Approach

2020 International Review of Management and Business Research  
Later on, (Swanson & Granger, 1997) for the first time used VAR residuals in PC algorithm to determine the causal ordering in time series.  ...  The results observed from GARCH-GJR model show spill over effect from the leading foreign stock markets toward Pakistan stock market excluding Sir Lanka.  ...  The results of GARCH and GJR model indicates that the return of Indian stock market (BSE-SENSEX) effect the return of Pakistan stock market (KSE-100) while the other two stock markets Bangladesh and Sir  ... 
doi:10.30543/9-4(2020)-37 fatcat:3i6qxq4iovatro6s6p2rtbgz3i
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